Adaptive Monte Carlo methods for rare event simulations

Winter Simulation Conference Proceedings

Volumn 1, Issue , 2002, Pages 108-115

  Hsieh, Ming Hua ^a  

^a NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ITERATIVE METHODS; MONTE CARLO METHODS; PARAMETER ESTIMATION; PROBABILITY; SAMPLING;

CROSS ENTROPY METHOD;

COMPUTER SIMULATION;

EID: 0036930617     PISSN: 02750708     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Al-Qaq, W. A., M. Devetsikiotis, and J. K. Townsend. 1995. Stochastic gradient optimization of importance sampling for the efficient simulation of digital communication systems. IEEE Transactions on Communications, 43:2975-2985.
2. Bratley, P., B. Fox, and L. Schrage. 1987. A Guide to Simulation. Springer-Verlag, New York, second edition.
3. Bucklew, J. 1990. Large Deviation Techniques in Decision, Simulation, and Estimation. John Wiley and Sons, Inc., New York.
4. de Boer, P.T., V. F. Nicola and R. Y. Rubinstein, 2000. Adaptive importance sampling simulation of queueing networks. Proceedings of the 2000 Winter Simulation Conference, J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds., 646-655. Institute of Electrical and Electronics Engineers.
5. Dembo, A. and O. Zeitouni. 1993. Large Deviation Techniques and Applications. Jones and Bartlett, Boston.
6. Devetsikiotis, M. and J. K. Townsend. 1993a. An algorithmic approach to the optimization of importance sampling parameters in digital communication system simulation. IEEE Transactions on Communications 41:1464-1473.
7. Devetsikiotis, M. and J. K. Townsend. 1993b. Statistical optimization of dynamic importance sampling parameters for efficient simulation of communication networks. IEEE/ACM Transactions on Networking 1:293-305.
8. Fu, M. C, and J. Q. Hu. 1997. Conditional Monte Carlo: Gradient estimation and optimization applications. Kluwer Academic Publisher, Boston.
9. Glasserman, P. 1991. Gradient estimation via perturbation analysis. Boston: Kluwer.
10. Glynn, P. W. 1986. Stochastic approximation for Monte Carlo optimization. In Proceedings of the 1986 Winter Simulation Conference, 356-365.
11. Glynn, P. W. 1990. Likelihood ratio gradient estimation for stochastic systems. Communications of the ACM 33:75-84.
12. Glynn, P. W. 1994. Efficiency improvement techniques. Annals of Oper. Res., 53:175-197.
13. Glynn, P. W. and D. L. Iglehart. 1989. Importance sampling for stochastic simulations. Manage. Sci., 35:1367-1392.
14. Hammersley, J. M. and D. C. Handscomb. 1965. Monte Carlo Methods. Methuen & Co. Ltd., London.
15. Heidelberger, P. 1995. Fast simulation of rare events in queueing and reliability models. ACM Trans. on Modeling and Computer Simulation, 5:43-85.
16. Lehtonen, T. and H. Nyrhinen. 1992. Simulating level-crossing probabilities by importance sampling. Adv. in Appl. Probab., 24(4):858-874.
17. Lieber, D., R.Y. Rubinstein and D. Elmakis. 1997. Quick estimation of rare events in stochastic networks. IEEE Trans. Rel., 46(2):254-265.
18. Jelinek, F. 1997. Statistical methods for Speech Recognition, Massachusetts Institute of Technology Press.
19. Kapur, J.N. and H. K.Kesavan. 1992. Entropy Optimization Principles with Applications. Academic Press.
20. Karlin, S. and H. M. Taylor. 1975. A First Course in Stochastic Processes. Academic Press, New York-London, second edition.
21. Kiefer, J., and J. Wolfowitz. 1952. Stochastic estimation of the maximum of a regression function. Annals of Mathematical Statistics, 23:462-466.
22. Robbins, H., and S. Monro. 1951. A stochastic approximation method. Annals of Mathematical Statistics, 22:400-407.
23. Rubinstein, R. Y. 1992. Sensitivity analysis of discrete event systems by the "push-out" method. Annals of Operations Research 39:229-237.
24. Rubinstein, R. Y. 1997. Optimization of computer simulation models with rare events. European Journal of Operations Research 99:89-112.
25. Rubinstein, R. Y. 1999. Rare event simulation via crossen-tropy and importance sampling. In Second International Workshop on Rare Event Simulation, RESIM 99, 1-17.
26. Rubinstein, R. Y. and B. Melamed. 1998. Modern Simulation and Modeling. Wiley.
27. Siegmund, D. 1976. Importance sampling in the Monte Carlo study of sequential tests. Ann. Statist., 4(4):673-684.
28. Su, Y., and M. Fu. 2000. Importance sampling in derivative securities pricing. Proceedings of the 2000 Winter Simulation Conference, J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds., 587-596. Institute of Electrical and Electronics Engineers.
29. Vazquez-Abad, F., and D. Dufresne. 1998. Accelerated simulation for pricing Asian options. Proceedings of the 1998 Winter Simulation Conference, D. J. Medeiros, E. F. Watson, J. S. Carson, and M. S. Manivannan, eds., 1493-1500. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.